add support for Step Predictor and Forecaster abstract Interfaces.

[kartikangiras]

Describe your changes

Summary of the changes.
Introduce two new abstract base classes that form the foundation of the refactored Neural-LAM architecture:

• StepPredictor: Maps (X_{t-1}, X_t, F_t) → X_{t+1} to a single-step neural network abstraction (GNN, CNN)

• Forecaster: Maps (init_states, forcing, true_states) to forecast over a full time window which is the forecasting strategy layer.

Please also include relevant motivation and context.

The current ARModelclass is a monolithic class that handles AR unrolling, batch management, loss computation, logging, and GNN forward passes all mixed together. This code change begins splitting those responsibilities into well-scoped, composable layers.

This enables:

• Clear separation of concerns (neural net architecture vs. forecasting strategy vs. Lightning orchestration).
• Support for future architectures (CNNs, ensemble models, non-AR strategies).
• Reusability of StepPredictor outside the training loop (inference, scripting).

List any dependencies that are required for this change.
None

Issue Link

relates to #49 (this is the first part of the entire implementation).
< Link to the relevant issue or task, if applicable > (e.g. closes #00 or solves #00)

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

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[joeloskarsson]

Following my comment in #49 , let's focus efforts in refactoring the model hierarchy on #208. Please share any ideas and contribute there :) To avoid duplicates I will close this for now.